Wald tests for IV regression with weak instruments
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | https://hdl.handle.net/10438/11222 |
Resumo: | This dissertation deals with the problem of making inference when there is weak identification in models of instrumental variables regression. More specifically we are interested in one-sided hypothesis testing for the coefficient of the endogenous variable when the instruments are weak. The focus is on the conditional tests based on likelihood ratio, score and Wald statistics. Theoretical and numerical work shows that the conditional t-test based on the two-stage least square (2SLS) estimator performs well even when instruments are weakly correlated with the endogenous variable. The conditional approach correct uniformly its size and when the population F-statistic is as small as two, its power is near the power envelopes for similar and non-similar tests. This finding is surprising considering the bad performance of the two-sided conditional t-tests found in Andrews, Moreira and Stock (2007). Given this counter intuitive result, we propose novel two-sided t-tests which are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test of Moreira (2003). |
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Vilela, Lucas PimentelEscolas::EPGEFGVMedeiros, Marcelo CunhaAlmeida, Caio Ibsen Rodrigues deMoreira, Marcelo Jovita2013-10-14T14:45:02Z2013-10-14T14:45:02Z2013-09-17VILELA, Lucas Pimentel. Wald tests for IV regression with weak instruments. Dissertação (Mestrado em Economia) - FGV - Fundação Getúlio Vargas, Rio de Janeiro, 2013.https://hdl.handle.net/10438/11222This dissertation deals with the problem of making inference when there is weak identification in models of instrumental variables regression. More specifically we are interested in one-sided hypothesis testing for the coefficient of the endogenous variable when the instruments are weak. The focus is on the conditional tests based on likelihood ratio, score and Wald statistics. Theoretical and numerical work shows that the conditional t-test based on the two-stage least square (2SLS) estimator performs well even when instruments are weakly correlated with the endogenous variable. The conditional approach correct uniformly its size and when the population F-statistic is as small as two, its power is near the power envelopes for similar and non-similar tests. This finding is surprising considering the bad performance of the two-sided conditional t-tests found in Andrews, Moreira and Stock (2007). Given this counter intuitive result, we propose novel two-sided t-tests which are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test of Moreira (2003).Esta dissertação trata do problema de inferência na presença de identificação fraca em modelos de regresso com variáveis instrumentais. Mais especificamente em testes de hipóteses com relação ao parâmetro da variável endógena quando os instrumentos são fracos. O principal foco é nos testes condicionais unilaterais baseados nas estatísticas de razão de máxima verossimilhança, score e Wald. Resultados teóricos e numéricos mostram que o teste t condicional unilateral baseado no estimador de mínimos quadrados em dois estágios tem uma boa performance mesmo na presença de instrumentos fracamente correlacionados com a variável endógena. A abordagem condicional corrige uniformemente o tamanho do teste t e quando a estatística F populacional é tão pequena quanto dois, o poder do teste é próximo ao power envelope tanto de testes similares quanto de não similares. Tal resultado é surpreendente visto a má performance dos testes t’s condicionais bilaterais relatada em (6, Andrews, Moreira and Stock (2007)). Dado esse resultado aparentemente contra intuitivo, apresentamos novos testes t’s condicionals bilaterais que são aproximadamente não viesados e performam, em alguns casos, tão bem quanto o teste condicional baseado na estatística de razão de verossimilhança de ( 19 , Moreira (2003)).engInstrumental variable regressionInvariant testsOptimal testsSimilar testsUnbiased testsWeak instrumentsRegressão com variáveis instrumentaisTestes invariantesTestes ótimosTestes similaresTestes não viesadosInstrumentos fracosEconomiaVariáveis instrumentais (Estatística)Análise de regressãoTestes de hipóteses estatísticasWald tests for IV regression with weak instrumentsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas 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|
dc.title.eng.fl_str_mv |
Wald tests for IV regression with weak instruments |
title |
Wald tests for IV regression with weak instruments |
spellingShingle |
Wald tests for IV regression with weak instruments Vilela, Lucas Pimentel Instrumental variable regression Invariant tests Optimal tests Similar tests Unbiased tests Weak instruments Regressão com variáveis instrumentais Testes invariantes Testes ótimos Testes similares Testes não viesados Instrumentos fracos Economia Variáveis instrumentais (Estatística) Análise de regressão Testes de hipóteses estatísticas |
title_short |
Wald tests for IV regression with weak instruments |
title_full |
Wald tests for IV regression with weak instruments |
title_fullStr |
Wald tests for IV regression with weak instruments |
title_full_unstemmed |
Wald tests for IV regression with weak instruments |
title_sort |
Wald tests for IV regression with weak instruments |
author |
Vilela, Lucas Pimentel |
author_facet |
Vilela, Lucas Pimentel |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.affiliation.none.fl_str_mv |
FGV |
dc.contributor.member.none.fl_str_mv |
Medeiros, Marcelo Cunha Almeida, Caio Ibsen Rodrigues de |
dc.contributor.author.fl_str_mv |
Vilela, Lucas Pimentel |
dc.contributor.advisor1.fl_str_mv |
Moreira, Marcelo Jovita |
contributor_str_mv |
Moreira, Marcelo Jovita |
dc.subject.eng.fl_str_mv |
Instrumental variable regression Invariant tests Optimal tests Similar tests Unbiased tests Weak instruments |
topic |
Instrumental variable regression Invariant tests Optimal tests Similar tests Unbiased tests Weak instruments Regressão com variáveis instrumentais Testes invariantes Testes ótimos Testes similares Testes não viesados Instrumentos fracos Economia Variáveis instrumentais (Estatística) Análise de regressão Testes de hipóteses estatísticas |
dc.subject.por.fl_str_mv |
Regressão com variáveis instrumentais Testes invariantes Testes ótimos Testes similares Testes não viesados Instrumentos fracos |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Variáveis instrumentais (Estatística) Análise de regressão Testes de hipóteses estatísticas |
description |
This dissertation deals with the problem of making inference when there is weak identification in models of instrumental variables regression. More specifically we are interested in one-sided hypothesis testing for the coefficient of the endogenous variable when the instruments are weak. The focus is on the conditional tests based on likelihood ratio, score and Wald statistics. Theoretical and numerical work shows that the conditional t-test based on the two-stage least square (2SLS) estimator performs well even when instruments are weakly correlated with the endogenous variable. The conditional approach correct uniformly its size and when the population F-statistic is as small as two, its power is near the power envelopes for similar and non-similar tests. This finding is surprising considering the bad performance of the two-sided conditional t-tests found in Andrews, Moreira and Stock (2007). Given this counter intuitive result, we propose novel two-sided t-tests which are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test of Moreira (2003). |
publishDate |
2013 |
dc.date.accessioned.fl_str_mv |
2013-10-14T14:45:02Z |
dc.date.available.fl_str_mv |
2013-10-14T14:45:02Z |
dc.date.issued.fl_str_mv |
2013-09-17 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
VILELA, Lucas Pimentel. Wald tests for IV regression with weak instruments. Dissertação (Mestrado em Economia) - FGV - Fundação Getúlio Vargas, Rio de Janeiro, 2013. |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10438/11222 |
identifier_str_mv |
VILELA, Lucas Pimentel. Wald tests for IV regression with weak instruments. Dissertação (Mestrado em Economia) - FGV - Fundação Getúlio Vargas, Rio de Janeiro, 2013. |
url |
https://hdl.handle.net/10438/11222 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
https://repositorio.fgv.br/bitstreams/144c8999-9bb2-4c24-aa92-0bb0a5611321/download https://repositorio.fgv.br/bitstreams/d487bac7-3a5d-41e4-aa5b-8330b09bc3f3/download https://repositorio.fgv.br/bitstreams/acab9bb4-2890-427c-a1ba-7676519e965a/download https://repositorio.fgv.br/bitstreams/ae353d97-7b6b-4e0c-b65e-99d7bfe6f038/download https://repositorio.fgv.br/bitstreams/21a5c31c-72fd-48af-bfdc-fde841e2bf22/download https://repositorio.fgv.br/bitstreams/5787d9f2-f607-46a1-a960-53f88d6d0dd5/download |
bitstream.checksum.fl_str_mv |
bf9a1d9c5f72ce6127f45a9b52b3975a dfb340242cced38a6cca06c627998fa1 0c6ccb4a87fe6410358adf1a0b1dd91d cc1542ce2f697b4ac186a90e7d5e9c9f aca35c07ad4423a59367417c35d917b8 dc055814d126c038d7fc486a417bf2e6 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
|
_version_ |
1813797631506251776 |